


'''simple docstring'''


# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				return 1 / (1 + np.exp(-z	))







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				return (-y * np.log(SCREAMING_SNAKE_CASE__	) - (1 - y) * np.log(1 - h	)).mean()







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			List[str]							= np.dot(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
				return np.sum(y * scores - np.log(1 + np.exp(SCREAMING_SNAKE_CASE__	)	)	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__=7_0000	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Dict							= np.zeros(x.shape[1]	)

				for iterations in range(SCREAMING_SNAKE_CASE__	):
								_SCREAMING_SNAKE_CASE					:			Tuple							= np.dot(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
								_SCREAMING_SNAKE_CASE					:			str							= sigmoid_function(SCREAMING_SNAKE_CASE__	)
								_SCREAMING_SNAKE_CASE					:			Any							= np.dot(x.T				, h - y	) / y.size
								_SCREAMING_SNAKE_CASE					:			List[Any]							= theta - alpha * gradient  # updating the weights
								_SCREAMING_SNAKE_CASE					:			List[Any]							= np.dot(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
								_SCREAMING_SNAKE_CASE					:			str							= sigmoid_function(SCREAMING_SNAKE_CASE__	)
								_SCREAMING_SNAKE_CASE					:			Dict							= cost_function(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
								if iterations % 100 == 0:
												print(f"""loss: {j} \t"""	)  # printing the loss after every 100 iterations
				return theta


# In[68]:

if __name__ == "__main__":
				UpperCAmelCase_						:  Union[str, Any]				  =						datasets.load_iris()
				UpperCAmelCase_						:  Union[str, Any]				  =						iris.data[:, :2]
				UpperCAmelCase_						:  Union[str, Any]				  =						(iris.target != 0) * 1

				UpperCAmelCase_						:  Union[str, Any]				  =						0.1
				UpperCAmelCase_						:  int				  =						logistic_reg(alpha, x, y, max_iterations=7_0000)
				print('theta: ', theta)  # printing the theta i.e our weights vector







				def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







								"""simple docstring"""





								return sigmoid_function(
								    np.dot(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)	)  # predicting the value of probability from the logistic regression algorithm

				plt.figure(figsize=(10, 6))
				plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='b', label='0')
				plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='r', label='1')
				((UpperCAmelCase_)  ,			(UpperCAmelCase_))						:  int				  =						(x[:, 0].min(), x[:, 0].max())
				((UpperCAmelCase_)  ,			(UpperCAmelCase_))						:  Optional[Any]				  =						(x[:, 1].min(), x[:, 1].max())
				((UpperCAmelCase_)  ,			(UpperCAmelCase_))						:  Optional[Any]				  =						np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
				UpperCAmelCase_						:  Optional[int]				  =						np.c_[xxa.ravel(), xxa.ravel()]
				UpperCAmelCase_						:  Optional[int]				  =						predict_prob(grid).reshape(xxa.shape)
				plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='black')

				plt.legend()







				plt.show()








'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_						:  Dict				  =						{
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  Optional[Any]				  =						['MobileBertTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  Optional[Any]				  =						[
				    'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MobileBertForMaskedLM',
				    'MobileBertForMultipleChoice',
				    'MobileBertForNextSentencePrediction',
				    'MobileBertForPreTraining',
				    'MobileBertForQuestionAnswering',
				    'MobileBertForSequenceClassification',
				    'MobileBertForTokenClassification',
				    'MobileBertLayer',
				    'MobileBertModel',
				    'MobileBertPreTrainedModel',
				    'load_tf_weights_in_mobilebert',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  List[str]				  =						[
				    'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFMobileBertForMaskedLM',
				    'TFMobileBertForMultipleChoice',
				    'TFMobileBertForNextSentencePrediction',
				    'TFMobileBertForPreTraining',
				    'TFMobileBertForQuestionAnswering',
				    'TFMobileBertForSequenceClassification',
				    'TFMobileBertForTokenClassification',
				    'TFMobileBertMainLayer',
				    'TFMobileBertModel',
				    'TFMobileBertPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_mobilebert import (
				    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MobileBertConfig,
				    MobileBertOnnxConfig,
				)
				from .tokenization_mobilebert import MobileBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mobilebert_fast import MobileBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilebert import (
								    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileBertForMaskedLM,
								    MobileBertForMultipleChoice,
								    MobileBertForNextSentencePrediction,
								    MobileBertForPreTraining,
								    MobileBertForQuestionAnswering,
								    MobileBertForSequenceClassification,
								    MobileBertForTokenClassification,
								    MobileBertLayer,
								    MobileBertModel,
								    MobileBertPreTrainedModel,
								    load_tf_weights_in_mobilebert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_mobilebert import (
								    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFMobileBertForMaskedLM,
								    TFMobileBertForMultipleChoice,
								    TFMobileBertForNextSentencePrediction,
								    TFMobileBertForPreTraining,
								    TFMobileBertForQuestionAnswering,
								    TFMobileBertForSequenceClassification,
								    TFMobileBertForTokenClassification,
								    TFMobileBertMainLayer,
								    TFMobileBertModel,
								    TFMobileBertPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase_						:  str				  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




